Dynamical analysis,circuit realization,and application in pseudorandom number generators of a fractional-order laser chaotic system

A new five-dimensional fractional-order laser chaotic system (FOLCS) is constructed by incorporating complex variables and fractional calculus into a Lorentz-Haken-type laser system. Dynamical behavior of the system, circuit realization and application in pseudorandom number generators are studied. Many types of multi-stable states are discovered in the system. Interestingly, there are two types of state transition phenomena in the system, one is the chaotic state degenerates to a periodical state, and the other is the intermittent chaotic oscillation. In addition, the complexity of the system when two parameters change simultaneously is measured by the spectral entropy algorithm. Moreover, a digital circuit is design and the chaotic oscillation behaviors of the system are verified on this circuit. Finally, a pseudo-random sequence generator is designed using the FOLCS, and the statistical characteristics of the generated pseudo-random sequence are tested with the NIST-800-22. This study enriches the research on the dynamics and applications of FOLCS.     

Superconductivity and unconventional density waves in vanadium-based kagome materials AV3Sb5

Recently, the discovery of vanadium-based kagome metal AV₃Sb₅ (A= K, Rb, Cs) has attracted great interest in the field of superconductivity due to the coexistence of superconductivity, non-trivial surface state and multiple density waves. In this topical review, we present recent works of superconductivity and unconventional density waves in vanadium-based kagome materials AV₃Sb₅. We start with the unconventional charge density waves, which are thought to correlate to the time-reversal symmetry-breaking orders and the unconventional anomalous Hall effects in AV₃Sb₅. Then we discuss the superconductivity and the topological band structure. Next, we review the competition between the superconductivity and charge density waves under different conditions of pressure, chemical doping, thickness, and strains. Finally, the experimental evidence of pseudogap pair density wave is discussed.     

Pattern transition and regulation in a subthalamopallidal network under electromagnetic effect

Although the significant roles of magnetic induction and electromagnetic radiation in the neural system have been widely studied, their influence on Parkinson's disease (PD) has yet to be well explored. By virtue of the magnetic flux variable, this paper studies the transition of firing patterns induced by magnetic induction and the regulation effect of external magnetic radiation on the firing activities of the subthalamopallidal network in basal ganglia. We find: (i) The network reproduces five typical waveforms corresponding to the severity of symptoms: weak cluster, episodic, continuous cluster, episodic, and continuous wave. (ii) Magnetic induction is a double-edged sword for the treatment of PD. Although the increase of magnetic coefficient may lead the physiological firing activity to transfer to pathological firing activity, it also can regulate the pathological intensity firing activity with excessive β-band power transferring to the physiological firing pattern with weak β-band power. (iii) External magnetic radiation could inhibit continuous tremulous firing and β-band power of subthalamic nucleus (STN), which means the severity of symptoms weakened. Especially, the bi-parameter plane of the regulation region shows that a short pulse period of magnetic radiation and a medium level of pulse percentage can well regulate pathological oscillation. This work helps to understand the firing activity of the subthalamopallidal network under electromagnetic effect. It may also provide insights into the mechanisms behind the electromagnetic therapy of PD-related firing activity.     

Quantum algorithm for neighborhood preserving embedding

Neighborhood preserving embedding (NPE) is an important linear dimensionality reduction technique that aims at preserving the local manifold structure. NPE contains three steps, i.e., finding the nearest neighbors of each data point, constructing the weight matrix, and obtaining the transformation matrix. Liang et al. proposed a variational quantum algorithm (VQA) for NPE [Phys. Rev. A 101 032323 (2020)]. The algorithm consists of three quantum sub-algorithms, corresponding to the three steps of NPE, and was expected to have an exponential speedup on the dimensionality n. However, the algorithm has two disadvantages: (i) It is not known how to efficiently obtain the input of the third sub-algorithm from the output of the second one. (ii) Its complexity cannot be rigorously analyzed because the third sub-algorithm in it is a VQA. In this paper, we propose a complete quantum algorithm for NPE, in which we redesign the three sub-algorithms and give a rigorous complexity analysis. It is shown that our algorithm can achieve a polynomial speedup on the number of data points m and an exponential speedup on the dimensionality n under certain conditions over the classical NPE algorithm, and achieve a significant speedup compared to Liang et al.'s algorithm even without considering the complexity of the VQA.     

Evolution of electrical conductivity and semiconductor to metal transition of iron oxides at extreme conditions

Iron oxides are widely found as ores in Earth's crust and are also important constituents of its interiors. Their polymorphism, composition changes, and electronic structures play essential roles in controlling the structure and geodynamic properties of the solid Earth. While all-natural occurring iron oxides are semiconductors or insulators at ambient pressure, they start to metalize under pressure. Here in this work, we review the electronic conductivity and metallization of iron oxides under high-pressure conditions found in Earth's lower mantle. We summarize that the metallization of iron oxides is generally controlled by the pressure-induced bandgap closure near the Fermi level. After metallization, they possess much higher electrical and thermal conductivity, which will facilitate the thermal convection, support a more stable and thicker D^{$\prime\prime$} layer, and formulate Earth's magnetic field, all of which will constrain the large-scale dynamos of the mantle and core.     

Bifurcation analysis of visual angle model with anticipated time and stabilizing driving behavior

In the light of the visual angle model (VAM), an improved car-following model considering driver's visual angle, anticipated time and stabilizing driving behavior is proposed so as to investigate how the driver's behavior factors affect the stability of the traffic flow. Based on the model, linear stability analysis is performed together with bifurcation analysis, whose corresponding stability condition is highly fit to the results of the linear analysis. Furthermore, the time-dependent Ginzburg-Landau (TDGL) equation and the modified Korteweg-de Vries (mKdV) equation are derived by nonlinear analysis, and we obtain the relationship of the two equations through the comparison. Finally, parameter calibration and numerical simulation are conducted to verify the validity of the theoretical analysis, whose results are highly consistent with the theoretical analysis.     

Comprehensive performance of a ball-milled La0.5Pr0.5Fe11.4Si1.6B0.2Hy/Al magnetocaloric composite

Due to the hydrogen embrittlement effect, La(Fe,Si)₁₃-based hydrides can only exist in powder form, which limits their practical application. In this work, ductile and thermally conductive Al metal was homogeneously mixed with La_0.5Pr_0.5Fe_11.4Si_1.6B_0.2 using the ball milling method. Then hydrogenation and compactness shaping of the magnetocaloric composites were performed in one step via a sintering process under high hydrogen pressure. As the Al content reached 9 wt.%, the La_0.5Pr_0.5Fe_11.4Si_1.6B_0.2H_y/Al composite showed the mechanical behavior of a ductile material with a yield strength of ~44 MPa and an ultimate strength of 269 MPa accompanied by a pronounced improvement in thermal conductivity. Due to the ease of formation of Fe-Al-Si phases and the several micron and submicron sizes of the composite particles caused by ball milling process, the magnetic entropy change of the composites was substantially reduced to ~1.2 J/kg· K-1.5 J/kg· K at 0 T-1.5 T.     

Crown evolution kinematics of a camellia oil droplet impacting on a liquid layer

The phenomenon of droplet impact on an immiscible liquid is encountered in a variety of scenarios in nature and industrial production. Despite exhaustive research, it is not fully clear how the immiscibility of the liquid on which a droplet impacts affects the crown evolution. The present work experimentally investigates the evolution kinematics of a crown formed by the normal impact of a camellia oil droplet on an immiscible water layer. Based on discussion of dynamic impact behaviors for three critical Weber numbers (We), the radius of the crown and its average spreading velocity are compared with those of previous theoretical models to discuss their applicability to the immiscible liquid. The evolution kinematics (morphology and velocity) are analyzed by considering the effects of the We and layer thickness. Furthermore, the ability of crown expansion in radial and vertical directions is characterized by a velocity ratio. The results show that our experimental crown radius still follows a square-root function of evolution time, which agrees with the theoretical predictions. The dimensionless average spreading velocity decreases with We and follows a multivariate power law, while the dimensionless average rising velocity remains constant. The velocity ratio is shown to linearly increase with We, demonstrating that the rising movement in crown evolution gradually enhances with We. These results are helpful for further investigation on the droplet impact on an immiscible liquid layer.     

物理学史中的一月

无数的人在世界各地搭机旅行,但却没想过飞机能一直飞在高空中的原因。除了其他的基本理论之外,我们应该把一些飞行原理的理论基础归功于一位瑞士数学家白努利(Daniel Bernoulli),他在流体力学,以及几率、统计学和弦振动方面做出了开创性的贡献。     

Hyperparameter on-line learning of stochastic resonance based threshold networks

Aiming at training the feed-forward threshold neural network consisting of nondifferentiable activation functions, the approach of noise injection forms a stochastic resonance based threshold network that can be optimized by various gradient-based optimizers. The introduction of injected noise extends the noise level into the parameter space of the designed threshold network, but leads to a highly non-convex optimization landscape of the loss function. Thus, the hyperparameter on-line learning procedure with respective to network weights and noise levels becomes of challenge. It is shown that the Adam optimizer, as an adaptive variant of stochastic gradient descent, manifests its superior learning ability in training the stochastic resonance based threshold network effectively. Experimental results demonstrate the significant improvement of performance of the designed threshold network trained by the Adam optimizer for function approximation and image classification.